Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients
Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients
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DOI：
10.1186/s12935-020-01267-y
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发表时间：
2020-05-20
影响因子：
5.8
通讯作者：
Chen, Bo
中科院分区：
文献类型：
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作者：
Qiu, Jieping;Sun, Mengyu;Chen, Bo
背景本研究的目的是对胃癌自噬相关基因进行生物信息学分析，构建胃癌预后预测的多基因联合标记。方法应用GO和KEGG分析胃癌组织中自噬相关基因的差异表达，并在Cytoscape软件中构建PPI网络。为了优化胃癌预后评估体系，我们建立了整合自噬相关基因的胃癌预后模型。我们使用单因素考克斯比例风险回归分析从Atlas癌症基因组（TCGA）胃癌队列中的204个自噬相关基因中筛选与预后相关的基因。然后，将生成的基因应用于最小绝对收缩和选择算子（LASSO）。最后将筛选出的基因进一步纳入多因素考克斯比例风险回归分析，建立预后模型。根据中位风险评分将患者分为高风险组和低风险组，进行生存分析，评价风险评分的预后价值。最后，结合临床病理特征和预后基因特征，建立了预测个体生存概率的诺模图。结果GO分析显示，28个差异表达的自噬相关基因在细胞生长、神经元死亡和细胞生长调控中富集。KEGG分析显示，28个差异表达的自噬相关基因与铂类耐药、细胞凋亡和p53信号通路有关。根据GRID 2、ATG 4D、GABARAPL 2、CXCR 4 4基因构建风险评分，根据总生存率将胃癌患者分为高危组和低危组。多因素考克斯回归分析中，危险度评分仍是独立的预后因素（HR = 1.922，95% CI = 1.573-2.349，P < 0.001）。累积曲线显示低危评分患者的生存时间明显长于高危评分患者（P < 0.001）。国外文献GSE 62254证明诺模图对个体胃癌患者的预后评估具有很强的能力。结论本研究为胃癌患者的预后判断及胃癌自噬的分子生物学研究提供了一个潜在的预后指标。
Background The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer. Methods GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 204 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinic-pathological features and prognostic gene signatures, a nomogram was established to predict individual survival probability. Results GO analysis showed that the 28 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 28 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients. Conclusions This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.